System and method for assessing the health of an asset

ABSTRACT

There are provided methods and systems for assessing the health of an asset. For example, a system is provided. The system may include a processor and a memory including instructions that, when executed by the processor, cause the processor to perform operations consistent with identifying a defect in a component of an asset. The operations may include fetching from an inspection system, a plurality of images acquired from an inspection of the component of the asset by the inspection system. The operations may include identifying, based on an image processing technique codified and included as part of the instructions, a subset of images from the plurality of images. The subset of images is representative of the defect in the component of the asset, and the image processing technique is selected from the group consisting of an auto-distress ranking technique, a structural similarity technique, a mean-subtracted filtering technique, and a Hessian norm computation technique.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of Indian Patent Application No.202011001847, filed Jan. 15, 2020.

TECHNICAL FIELD

The present disclosure relates to inspection of assets. Moreparticularly, the present disclosure relates to systems and methods forassessing the health or performance of an asset or of one or more of itssub-components.

BACKGROUND

In many industrial applications, routine inspections of assets can helpin extending the lifetime of these assets as the inspections can revealdamaged parts that either need to be replaced or serviced. For example,in aviation applications, engines are routinely inspected in order tomonitor their overall health and performance. When inspecting an engine,a borescope-based inspection (BSI) may be conducted to look at variousengine sub-components; the BSI typically includes capturing a sequenceof images, each image being a frame that can be analyzed to characterizeone or more aspects of the sub-component depicted in the frame.

In one exemplary use case, a typical BSI system may capture a video witha probe inserted in the engine in order to reach a sub-component ofinterest; then, by trial and error, a highly trained operator of the BSIsystem may select a good view of the sub-component based on the video ora frame of the video. The operator may then move on to nextsub-component by actuating the probe to another location within theengine. These typical steps in BSI methods result in the capture of manyimages, of which only a few show the best views of sub-componentdefects. Decisions about the condition of the sub-component aretypically made by experts based on these selected few frames. As such,the quality of the inspection depends highly on being able to adequatelylocate these frames of interest out of many captured frames; thisprocess is inherently difficult, and it depends subjectively on thetechnician's skills. Thus, current inspection methods are not-onlyinefficient but they can also be error-prone.

SUMMARY

The embodiments featured herein help solve or mitigate the above-notedissues as well as other issues known in the art. For example, in oneembodiment there is provided a system for identifying a defect in acomponent of an asset. The system includes a processor and a memoryincluding instructions that, when executed by the processor, cause theprocessor to perform operations consistent with identifying the defect.For instance, the operations may include fetching from an inspectionsystem, a plurality of images acquired from an inspection of thecomponent of the asset by the inspection system. The operations mayinclude identifying, based on an image processing technique codified andincluded as part of the instructions, a subset of images from theplurality of images. The subset of images is representative of thedefect in the component of the asset, and the image processing techniqueis selected from the group consisting of an auto-distress rankingtechnique, a structural similarity technique, a mean-subtractedfiltering technique, and a Hessian norm computation technique.

In another embodiment, there is provided a method for identifying adefect in a component of an asset. The method includes fetching, by adefect-identification system, from an inspection system, a plurality ofimages acquired from an inspection of the component of the asset by theinspection system. The method further includes identifying, by thedefect-identification system, based on an image processing technique, asubset of images from the plurality of images. The subset of images isrepresentative of the defect in the component of the asset, and theimage processing technique is selected from the group consisting of anauto-distress ranking technique, a structural similarity technique, amean-subtracted filtering technique, and a Hessian norm computationtechnique.

Additional features, modes of operations, advantages, and other aspectsof various embodiments are described below with reference to theaccompanying drawings. It is noted that the present disclosure is notlimited to the specific embodiments described herein. These embodimentsare presented for illustrative purposes only. Additional embodiments, ormodifications of the embodiments disclosed, will be readily apparent topersons skilled in the relevant art(s) based on the teachings provided.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments may take form in various components andarrangements of components. Illustrative embodiments are shown in theaccompanying drawings, throughout which like reference numerals mayindicate corresponding or similar parts in the various drawings. Thedrawings are only for purposes of illustrating the embodiments and arenot to be construed as limiting the disclosure. Given the followingenabling description of the drawings, the novel aspects of the presentdisclosure should become evident to a person of ordinary skill in therelevant art(s).

FIG. 1 illustrates a system according to several aspects describedherein.

FIG. 2 illustrates a method according to several aspects describedherein.

FIG. 3 illustrates a method according to several aspects describedherein.

FIG. 4 illustrates a method according to several aspects describedherein.

FIG. 5 illustrates a system according to several aspects describedherein.

DETAILED DESCRIPTION

While the illustrative embodiments are described herein for particularapplications, it should be understood that the present disclosure is notlimited thereto. Those skilled in the art and with access to theteachings provided herein will recognize additional applications,modifications, and embodiments within the scope thereof and additionalfields in which the present disclosure would be of significant utility.

For instance, one or more embodiments featured herein may be a systemthat automatically determine the best frames from a collection of framesacquired by an inspection system. In one embodiment, the best frames(e.g., one or more frame of interest) are identified by firstpre-processing the sequence of images using a mean subtracted filter,and then monitoring the Hessian norm of the image information. The“best” frames may be selected based on one or more criteria, such as,for example, a result of the aforementioned one or more pre-processingoperation. In another embodiment, an exemplary system can be configuredto select frames from a video based on the maximum exposure of differentcomponents; the selection may be achieved using a structural similaritymethod or the like. As such, the embodiments remove subjectivity fromthe inspection process and thus provide consistency and accuracy.

In the exemplary embodiments, a distress ranking method is not neededfor all the frames captured, and this makes the health assessment of anengine ˜100 times faster than the typical inspection methods, inaddition to improving the accuracy of the inspection. The embodimentsalso provide a training dataset for distress ranking algorithms, shouldan operator decide to use such algorithms in assessing engine health andperformance. As such, the embodiments can reduce training time inaddition to increasing reliability.

The embodiments confer several advantages. For example, one or moreembodiments described herein may automatically estimate the extent ofdefects registered during an inspection. Furthermore, the embodimentsmay automatically provide additional informative statistics about thedefects, thus providing means for a continuous-time and/orcontinuous-valued defect metric. The embodiment thus help re-defineranking procedures, and thus, they reduce the error in currentprocesses, which cannot be discretized with a ranking in steps of <1(continuous).

The embodiments also allow the tracking of distress progression, andthey thus help in forecasting part replacements and optimizing scheduledmaintenance cycles or the on-time delivery of assets. Furthermore, theembodiments allow continuous distress ranking methods (DRM) to beperformed, and when combined with operational parameters, theembodiments can help in establishing distress trends and forecasting.

Additionally, in one or more exemplary embodiments, acquired images canbe rendered on to a computer-aided design (CAD) model to enable aninspector to assess the actual distress relative to the as-designed (oras-manufactured) sub-component being examined. In the alternative, anexemplary system can be configured to use the composite images (i.e.,the images rendered onto the CAD model) to automatically (i.e., withoutuser intervention) assess the actual distress relative to theas-designed or as-manufactured sub-component being examined.

Generally, the embodiments include smart and on-the-fly component healthmonitoring systems that combine programmable inspection hardware inconjunction with embedded image processing techniques to characterizepart failure and automatically provide recommendations consideringhistorical and physics-based observations. Further, Generally, theembodiments help reduce the time required for engine assessment, andthey improve the reliability of an assessment. Furthermore, theembodiments help enhance the reliability of auto-distress rankingalgorithms, and they also help reduce the time required to preparedatasets for training auto-distress ranking algorithms.

The embodiments include an on-the fly recommendation system that enablescomponent distress ranking through seamless integration of advanced dataacquisition, analytics techniques and physics-based considerations. Theycan permit key frame extraction that provide engineers with new andreduced but accurate datasets that can be used to build more efficientanalytics based on engine health. They embodiments also provide astandardized and consistent images that can be stored for later use. Theembodiments also provide improved predictive models as a result of thebuilt-in quantification of the number of connected regions and thedistribution of defect areas. The embodiments also permit the automaticunification of design, operating and maintenance data with imageanalytics and physics-based data, resulting in enhanced predictiveanalytics. The latter feature reduces the cumbersome manual effort oftypical inspection procedures, by leveraging and customizing deeplearning methods for analytics.

The embodiments allow the real-time streaming of the image acquisition,which leads to fast, scalable, efficient and lightweight image or videoprocessing. This enables a wide variety of down-stream analytics toperformed, yielding improved component assessments. Generally, theembodiment's performance, and thus the quality of an inspection, is notdependent on skill and knowledge of the inspection engineer.

Exemplary embodiments can also be configured to identify a key frame ofinterest based on at least five steps. For example, summarizedinformation of every frame (e.g., a Hamming norm) may be used to make adecision. Additional information may be provided using a structuralsimilarity method, which is independent of absolute pixel values andworks using internal dependencies of the pixel values. Such an approachis not sensitive to the component external structural changes likecracks, discoloration etc.

The embodiments can also help quantify defects. This is achieved by analgorithm that measures the pixels marked as defects by anidentification machine learning model. Different statistics measureslike the number of connected regions, the distribution of these, and thelargest affected region may be provided to improve downstream analytics.

In addition to the aforementioned technical points of novelty andtechnical advantages, the embodiments also have several commercialadvantages, which address currently unmet needs in the industry. Forexample, the embodiments provide speedier inspections, thereby avoidingspending more time to find the best view of a component in a videoacquired through inspection. This will reduce misses of defects that aredue to operator oversight, which could be detrimental as further damagescould incur if a faulty component is missed during inspection. Thus, theembodiments decrease repair costs and improve the time-on-wing ofaircraft engines.

The embodiments may make use of image recognition algorithms such as apattern recognition algorithms, and a frame may be determined as beingof interest if a specific condition is met while processing the imagesusing the algorithms. For example, such a criterion may be the meansquare error (MSE). Generally, however, an exemplary method or systemmay independent of absolute pixel values and may work using internaldependencies of the pixel values because pixel dependencies carryimportant information about the structure of the objects in the visualscene. In the embodiments, CNNs may be used as a deep learning techniqueto directly train deep neural networks that can quantify the damage ofthe component directly instead of achieving component assessment in twosteps (detection and quantification).

Having described the characteristics and several advantages of theembodiments in general terms, an exemplary embodiment is now discussedin regards to FIG. 1 . The exemplary system 100 depicted in FIG. 1includes a plurality of components and subsystems that are configured toperform an inspection of an asset 111 and detect one or more defects ofa component 101 of the asset 111, without user intervention. The system100 includes an assessment system 108, which is configured to determinefrom a video 102 (or a collection of images 104 a, 104 c, and 104 n), areference frame 104 (or image). The inspection system 103 may be, forexample and not by limitation, a borescope inspection system.

The inspection system 103 is configured to acquire the video 102 via aprobe that is inserted in the asset 111, specifically to inspect thecomponent 101. In one exemplary use case, the probe may be actuated suchthat the video 102 includes several views of the component 101 and/orseveral views of a plurality of components like the component 101. Theinspection system 103 may further be configured to provide the referenceframe 104, which may be, a set of frames representative of known-to-bedefect-free portion of the component 101. For example, the referenceframe 104 may have been saved in a memory of the inspection system 103at a time when the asset 111 or the component 101 was firstcommissioned, or it may be an image of a similar component that is inpristine condition.

The system 100 further includes an assessment system 108 that isconfigured to fetch the video 102 and the reference frame 104 from theinspection system 103. The assessment system 108 is further configuredto determine from the collection of images (i.e., frames) from the video102 to select key frames of interest, i.e., a subset of images 110 thateach correspond to a defect of the component 101. A defect may be,herein, a deformation ensued from prolonged use, a crack, or any othernon-ideal structural changes that may increase the risk of the component101 failing during the operation of the asset 111.

The operation and various aspects of the system 100 are described hereinafter in terms of the inspection of one or more aviation-relatedcomponents. As construed herein, a video is a sequence of images (i.e.frames) and the quality of the information that can be ascertained fromthe sequence of images depends on may factors. For example, for aborescope video of aviation engines, some of the factors may be,non-exhaustively, the handling of the camera, lighting conditions, theaccessibility to the component of interest, the angle of capture, thelocation of capture, specific movements of the proves, as well as thecamera specifications. The system 100 is configured to identify keyframes of interest, in spite of these factors that may yield to a poorquality of the video 102.

As a non-limiting example, the system 100 is described in the context ofan inspection of one or more blades of an engine. In a typicalinspection, a probe of the inspection system 103 is introduced into theengine through the borescope port near the blades and held in place at aconvenient location. The blade set is then externally rotated slowly(usually manually) so that the blades rotate around their axis. What iscaptured in the video is a blade coming into focus from the background,partly visible initially, then slowly coming completely into focus andthen moving out of visibility, as the next blade comes into view. So,there is a location/time where there is maximum visibility of the blade.The latter situation is capable of yielding the best view for a blade tobe properly determined.

The system 100 is configured to identify these key frames thatcorrespond to the best views of the blades, out of all the framescaptured by the borescope. This identification is carried out accordingto an exemplary method 200 that can be executed by the assessment system108, as follows. The exemplary method 200, as shown in in FIG. 2 . Themethod 200 begins at step 202 and features a step 204 that includesapplying a mean subtraction filter on each frame to normalize thelighting/illumination of the component (i.e., in this example, a bladethat is in the field of view of the borescope's camera). Other filterslike a gaussian smoothening can also be used in conjunction or in lieuof the mean subtraction filter.

The method 200 features a step 206 that includes converting each frameacquired (or each frame under investigation) to grayscale, to furthernormalize across all sequence of frames that are being analyzed. Themethod 200 further includes generating a measure that summarizes theinformation for each of the frame (step 208). For example, and not bylimitation, a Hamming norm may be used to summarize the information fora particular the statistic. The method 200 further includes (at step210) generating a temporal map between the summarized information andthe set of frames that are examined. In one embodiment, the assessmentsystem 108 is configured to determine whether the temporal map exhibitsa periodic trend, like, for example and not by limitation, a sinusoid.The method 200 includes identifying and/or collecting all framescorresponding to a maximum position in the detected periodic trend (step212). The frames corresponding to these maxima are to the frames whichhave most of the image showing the blade. In other words, framescorresponding to the maximum value of every period of the sequentialHamming norm are the key frames required. These are frames with themaximum desired information. Likewise, the frames corresponding to theminimum value of every period have less information about the blade, butmore information about the background, which in some embodiments mayalso be of inspection value. The method 200 can then include issuing thecollected frames (step 214) and ending at step 216.

In another embodiment, the assessment system 108 may be configured toovercome the problem of identifying frames with maximum exposure basedon a structural similarity method. In this embodiment, the structuralsimilarity method is independent of absolute pixel values and worksusing internal dependencies of the pixel values, hence the assessmentsystem 108 is able to identify frames with maximum exposure of differentcomponents and is not sensitive to the component external structuralchanges like cracks, discoloration etc. In this method, spatially closepixels in the images will have strong internal dependencies and hencecarry important information about the structure of the objects in thevisual scene. The exemplary method 200 may thus include threshold tuningbased on the video quality to filter the right frames and internalthreshold tuning to avoid flagging multiple frames of same component.

In yet another embodiment, the system 100 is configured to quantify thedefect that have been detected on a component in a manner that willimprove current damage analytic models. For instance, a machine learningalgorithm for defect identification may be used on an input image tomark defect areas. In the above-mentioned example of the bladeinspection, a key frame detected by the assessment system 108 and thenprocessed via a defect detection module of the assessment system 108that marks out the pixels in the original image which correspond todefects in the blade.

For example, these defects are from a pretrained set of spallation,oxidation, cracking, material removal or others. The assessment system108 uses this set as an input to conduct a method 300 (FIG. 3 ) toquantify these defects. The method 300 begins at step 302 and includessegregating the input to a binary image corresponding to every defectmode of interest (step 304) from a training set. The method 300 furtherincludes performing the same segregation from every frame acquired fromthe inspection (step 306). In the binary image, an area that is a defectis a pixel with value +1, and unaffected area is a pixel with value 0(step 308). It is noted that such an assignment of the binary values toareas of defect and no-defect is by convention and thus not limiting.

The method 300 includes finding the number of connected regions (310). Aconnected region is a collection of pixels which have a value +1 andwhich are touching each other, such that within a connected region onecan traverse from any starting pixel to any other pixel in the regionwithout skipping any pixel. There can be any number of such connectedregions depending on the component and the defect. The method 300 thenprovides the distribution of the area of the connected regions for everydefect in every image (312). The method 300 also provides the cumulativesum of these connected regions. These then are used to better representthe condition of the component in the engine. A difference in thedistribution between different blades, for example, indicates adifferent health level of each blade. As such the method 300 helpsimproves analytic models that are used to predict life, servicing orremoval of a component, as defects can be quantified and thuscategorized utilizing the exemplary method 300 and the system 100. Themethod 300 then ends at step 314.

In yet another embodiment, the system 100 may be configured to execute amethod 400 (FIG. 4 ). The method 400 includes annotated (marked areaswhich denotes detected damage areas in visual image or frames) damagemodes in a visual form, either as in the images or video frames formats.The method 400 includes may make use of image processing techniques todetermine a set features including, but not limited to: part damageextent (area or length), nature (specific to damage mode: example:expanding vs localized), geometrical attributes such as shape, texture(smooth or abrasive or patterned or granular), form (continuous orwavy), color (gradient variations and patterns in shades), orientationwith respect to image edges, and a severity metric defined for each ofthe damage modes.

Generally, the method 400 includes providing annotations to the visualinputs that help categorize, i.e. distinguish, each of theaforementioned damage modes. These annotated features of the image orframes are processed at the pixel level to extract relevant metrics ofeach of the damage mode mentioned above. Specifically, the method 400begins at step 402 and includes extracting specific channel pixel values(e.g., RGB), thus performing a channel split (step 404). The method 400further includes identify, based on pixel values for example, colorgradients across the image or frame area (step 406).

The method 400 further includes ascertaining the intelligence coded inthe channels as heuristics that pertains to specific damage modes interms of segmenting or isolating regions that matches with heuristicconditions (step 408). These may include: crop, mask, smoothen, addnoise, blend, geometric transformations or convolutions. The method 400then includes (step 410) generating, from the above identified regions,metrics of damage modes such as shape, form, geometric attributes,texture, and color, for example. In addition to the above operations, toquantify the extent of damage, the method 400 can include usingoperations such as max-contiguous region identification of largeisolated regions within an image (step 412). The method 400 furtherincludes summarizing, using the information of these individualmax-contiguous regions identified for each image as, metrics includingtotal Pixel area, percentage of area, max-contiguous pixel area (step414). These metrics provide zone-wise (location) and componentinformation based on specific criteria. The method 400 further includesgenerating a set of rules and a knowledge repository database modulethat maintains necessary information for different components and damagemodes and input requirements. The method 400 further includes generatingone or more reports or outputs for each of the damage mode and alocation within a frame or image with quantification informationconsistent with the above-mentioned metrics (step 416), and the method400 ends at step 418. The format of the one or more outputs or reportsmay be in a specified format that is compliant with down-stream systems(e.g., analytics, diagnostics, safety, operations, maintenanceplanning).

FIG. 5 illustrates a system 500 according to an exemplary embodiment.The system 500 may be configured to implement one or more of the methodsfor defect-identification described above. The system 500 includes anapplication-specific processor 514 configured to perform tasks specificto assessing the health and/or performance of an asset. The processor514 has a specific structure imparted by instructions stored in a memory502 and/or by instructions 518 that can be fetched by the processor 514from a storage medium 520. The storage medium 520 may be co-located withthe processor 514, or it may be located elsewhere and be communicativelycoupled to the processor 514 via a communication interface 516.

The system 500 can be a stand-alone programmable system, or it can be aprogrammable module located in a much larger system, which itself may becentralized or distributed across various locations or computinginfrastructure, the latter being for example, a cloud-based computinginfrastructure. The processor 514 may include one or more hardwareand/or software components configured to fetch, decode, execute, store,analyze, distribute, evaluate, and/or categorize information.Furthermore, the processor 514 can include an input/output module (I/Omodule 512) that can be configured to ingest data pertaining to singleassets or fleets of assets. The processor 514 may include one or moreprocessing devices or cores (not shown). In some embodiments, theprocessor 514 may be a plurality of processors, each having either oneor more cores. The processor 514 can be configured to executeinstructions fetched from the memory 502, i.e. from one of memory block504, memory block 506, memory block 508, and memory block 510.

Without loss of generality, the storage 520 and/or the memory 502 mayinclude a volatile or non-volatile, magnetic, semiconductor, tape,optical, removable, non-removable, read-only, random-access, or any typeof non-transitory computer-readable computer medium. The storage medium520 may be configured to log data processed, recorded, or collectedduring the operation of the processor 514. The data may be time-stamped,location-stamped, cataloged, indexed, or organized in a variety of waysconsistent with data storage practice. The storage 520 and/or the memory502 may include programs and/or other information that may be used bythe processor 514 to perform tasks consistent with the processes and/ormethods described herein.

For example, and not by limitation, the processor 514 may be configuredby instructions from the memory block 506, the memory block 508, and thememory block 510, to perform operations resulting in either theidentification of a subset of images 507 representative of one or moredefects 513 from a component 501 of an asset 511. The processor 514 mayexecute the aforementioned image processing instructions 515 from memoryblocks 506, 508, and 510, which would cause the processor 514 to performcertain operations associated with monitoring the health and/orperformance of a component of an engine. The operations may includefetching from an inspection system 500, a plurality of images 505acquired from an inspection of a component 501 of the asset 511 by theinspection system 503. The operations may include identifying, based onan image processing technique codified and included as part of theinstructions in the memory blocks 506, 508, and 510, a subset of images507 from the plurality of images 505. The subset of images 507 isrepresentative of a defect in the component 501 of the asset 511. Theimage processing technique is selected from the group consisting of anauto-distress ranking technique, a structural similarity technique, amean-subtracted filtering technique, and a Hessian norm computationtechnique.

It is noted that while the embodiments have been described in thecontext of aviation applications and with BSI methods, they can be usedin a wide variety of industrial applications where inspections areperformed and not necessarily with BSI. As such, those skilled in therelevant art(s) will appreciate that various adaptations andmodifications of the embodiments described above can be configuredwithout departing from the scope and spirit of the disclosure.Therefore, it is to be understood that, within the scope of the appendedclaims, the disclosure may be practiced other than as specificallydescribed herein.

What is claimed is:
 1. A system for identifying a defect in a componentof an asset, the system comprising: a processor; a memory includinginstructions that, when executed by the processor, cause the processorto perform operations comprising: fetching from an inspection system, aplurality of images acquired from an inspection of the component of theasset by the inspection system; identifying, based on an imageprocessing technique codified and included as part of the instructions,a subset of images from the plurality of images, wherein the imageprocessing technique includes utilizing a Hamming norm to obtainsummarized information of each of the plurality of images, and thesubset of images is identified by using the summarized information fromthe Hamming norm, wherein the subset of images is representative of thedefect in the component of the asset, and wherein the image processingtechnique is selected from the group consisting of an auto-distressranking technique, a structural similarity technique, a mean-subtractedfiltering technique, and a Hessian norm computation technique.
 2. Thesystem of claim 1, wherein the operations further include fetching a CADmodel of the component and rendering a specified image from the subsetof images onto the CAD model.
 3. The system of claim 2, wherein theoperations further include fetching the CAD model from a databasecommunicatively coupled to the system.
 4. The system of claim 2, whereinthe operations further include identifying one or more regions of thespecified image representative of the defect.
 5. The system of claim 1,wherein the asset is an engine.
 6. The system of claim 1, wherein theinspection system includes a borescope inspection system.
 7. The systemof claim 1, wherein the operations further include extracting theplurality of images from a video.
 8. The system of claim 1, wherein theplurality of images include several views of the component.
 9. Thesystem of claim 1, wherein the operations further include providing ametric associated with the defect based on a specified image form thesubset of images.
 10. The system of claim 9, wherein the metric isindependent of an absolute pixel value of a specified image from thesubset of images.
 11. The system of claim 9, wherein the metric is basedon summarized information from a specified image from the subset ofimages.
 12. The system of claim 1, wherein the operations furtherinclude providing a dataset including metrics generated from the subsetof images based on the image processing technique.
 13. The system ofclaim 12, wherein the operations further include training a neuralnetwork or a deep learning system based on the dataset.
 14. The systemof claim 13, wherein the operations further include providing another asecond dataset based on a result of the training, the other seconddataset being representative of a predictive performance of thecomponent.
 15. A method for identifying a defect in a component of anasset, the method comprising: fetching, by a defect-identificationsystem, from an inspection system, a plurality of images acquired froman inspection of the component of the asset by the inspection system;identifying, by the defect-identification system, based on an imageprocessing technique, a subset of images from the plurality of images,wherein the image processing technique includes utilizing a Hamming normto obtain summarized information of each of the plurality of images, andthe subset of images is identified by using the summarized informationfrom the Hamming norm, wherein the subset of images is representative ofthe defect in the component of the asset, and wherein the imageprocessing technique is selected from the group consisting of anauto-distress ranking technique, a structural similarity technique, amean-subtracted filtering technique, and a Hessian norm computationtechnique.
 16. The method of claim 15, further including fetching a CADmodel of the component and rendering a specified image from the subsetof images onto the CAD model.
 17. The method of claim 16, furtherincluding fetching the CAD model from a database communicatively coupledto the system.
 18. The method of claim 15, further including extractingthe plurality of images from a video.
 19. The method of claim 15,further including providing a metric associated with the defect based ona specified image of the subset of images.